Browse by author
Lookup NU author(s): Dr Andrea Coraddu
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
In this paper the authors investigate the problems of predicting the fuel consumption and of providing the best value for the trim of a vessel in real operations based on data measured by the onboard automation systems. Three different approaches for the prediction of the fuel consumption are compared: White, Black and Gray Box Models. White Box Models (WBM) are based on the knowledge of the physical underling processes. Black Box Models (BBMs) build upon statistical inference procedures based on the historical data collection. Finally, the authors propose two different Gray Box Model (GBM) which are able to exploit both mechanistic knowledge of the underlying physical principles and available measurements. Based on these predictive models of the fuel consumption a new strategy for the optimisation of the trim of a vessel is proposed. Results on real world operational data show that the BBM is able to remarkably improve a state-of-the-art WBM, while the GBM is able to encapsulate the a-priory knowledge of the WBM into the BBM so to achieve the same performance of the latter but requiring less historical data. Moreover, results show that the GBM can be used as an effective tool for optimising the trim of a vessel in real operational conditions.
Author(s): Coraddu A, Oneto L, Baldi F, Anguita D
Publication type: Article
Publication status: Published
Journal: Ocean Engineering
Year: 2017
Volume: 130
Pages: 351-370
Print publication date: 15/01/2017
Online publication date: 13/12/2016
Acceptance date: 28/11/2016
ISSN (print): 0029-8018
Publisher: Elsevier
URL: https://doi.org/10.1016/j.oceaneng.2016.11.058
DOI: 10.1016/j.oceaneng.2016.11.058
Altmetrics provided by Altmetric